Jordan B. Strom

Diagnostic accuracy of CD4 cell count increase for virologic response after initiating highly active antiretroviral therapy.

Objective:To derive and internally validate a clinical prediction rule for virologic response based on CD4 cell count increase after initiation of HAART in a resource-limited setting. Design and methods:A retrospective cohort study at two HIV care clinics in Gaborone, Botswana. The participants were previously treatment-naive HIV-1-infected individuals initiating HAART. The main outcome measure was a plasma HIV-1 RNA level (viral load) ≤ 400 copies/ml (i.e. undetectable) 6 months after initiating HAART. Results:The ability of CD4 cell count increase to predict an undetectable viral load was significantly better in those with baseline CD4 cell counts ≤ 100 cells/μl [area under the ROC curve (AUC), 0.78; 95% confidence interval (CI), 0.67–0.89; versus AUC, 0.60; 95% CI, 0.48–0.71; P = 0.018]. The sensitivity, specificity, and positive and negative predictive values of a CD4 cell count increase of ≥ 50 cells/μl for an undetectable viral load in those with baseline CD4 cell counts ≤ 100 cells/μl were 93.1, 61.3, 92.5 and 63.3%, respectively. Alternatively, these values were 47.8, 87.1, 95.0 and 24.5%, respectively, if a increase in CD4 cell count of ≥ 150 cells/μl was used. Conclusions:CD4 cell count increase after initiating HAART has only moderate discriminative ability in identifying patients with an undetectable viral load, and the predictive ability is lower in patients with lower baseline CD4 cell counts. Although HIV treatment programs in resource-constrained settings could consider the use of CD4 cell count increases to triage viral load testing, more accurate approaches to monitoring virologic failure are urgently needed.

Causes of Short-Term Readmission After Percutaneous Coronary Intervention

Background—Rehospitalization within 30 days after an admission for percutaneous coronary intervention (PCI) is common, costly, and a future target for Medicare penalties. Causes of readmission after PCI are largely unknown. Methods and Results—To illuminate the causes of PCI readmissions, patients with PCI readmitted within 30 days of discharge between 2007 and 2011 at 2 hospitals were identified, and their medical records were reviewed. Of 9288 PCIs, 9081 (97.8%) were alive at the end of the index hospitalization. Of these, 893 patients (9.8%) were readmitted within 30 days of discharge and included in the analysis. Among readmitted patients, 341 patients (38.1%) were readmitted for evaluation of recurrent chest pain or other symptoms concerning for angina, whereas 59 patients (6.6%) were readmitted for staged PCI without new symptoms. Complications of PCI accounted for 60 readmissions (6.7%). For cases in which chest pain or other symptoms concerning for angina prompted the readmission, 21 patients (6.2%) met criteria for myocardial infarction, and repeat PCI was performed in 54 patients (15.8%). The majority of chest pain patients (288; 84.4%) underwent ≥1 diagnostic imaging test, most commonly coronary angiography, and only 9 (2.6%) underwent target lesion revascularization. Conclusions—After PCI, readmissions within 30 days were seldom related to PCI complications but often for recurrent chest pain. Readmissions with recurrent chest pain infrequently met criteria for myocardial infarction but were associated with high rates of diagnostic testing.

Out-of-pocket costs of HAART limit HIV treatment responses in Botswana's private sector.

A large number of HIV-infected patients in sub-Saharan Africa pay out-of-pocket for HAART. This analysis from Botswana indicates that higher median out-of-pocket regimen costs to patients for the initial 30 days of HAART are associated with failure to achieve a viral load< 400 copies/ml [US

Association Between Operator Procedure Volume and Patient Outcomes in Percutaneous Coronary Intervention A Systematic Review and Meta-Analysis

32; interquartile range (IQR), 20–84 compared with US

Clinical Preventability of 30-Day Readmission After Percutaneous Coronary Intervention

22; (IQR, 17–36), P = 0.001]. HAART costs should be minimized as scale-up efforts in sub-Saharan Africa progress.

Enhancing the Prediction of 30-Day Readmission After Percutaneous Coronary Intervention Using Data Extracted by Querying of the Electronic Health Record

Background—The growth of centers capable of performing percutaneous coronary intervention (PCI) has outpaced population growth despite declining incidence of myocardial infarction and prevalence of coronary artery disease, potentially increasing the proportion of operators falling below minimal yearly volume standards set by professional societies. Methods and Results—Electronic literature search of MEDLINE and the Cochrane Library for English-language articles published between 1977 and November 2012 was performed. Title and abstract review followed by full-text and references review were performed by 2 authors independently to identify studies examining the association between operator volume and outcomes in PCI. Using a standardized form, 2 authors abstracted information on study design, methods, outcomes, statistical methods, and conclusions. Studies were categorized according to methodological quality and outcomes. Meta-analyses were performed by outcome using a random-effects model. Of the 23 studies included in the analysis, 14 (61%) evaluated mortality, 7 (30%) evaluated major adverse cardiac events, and 2 (9%) evaluated angiographic success. In total, the studies evaluated 15 907 operators performing 205 214 PCIs on 1 109 103 patients at 2456 centers with a mean follow-up of 2.8 years. Eleven (48%) were considered higher quality. Studies with higher methodological quality and large sample sizes more often showed a relationship between operator volume and outcomes in PCI. Higher volume was associated with improved major adverse cardiac events at every threshold, regardless of the threshold evaluated. Conclusions—Mortality and major adverse cardiac events increase as operator volumes decrease in PCI. Among studies showing a relationship, high-volume operators were defined variably, with annual PCIs ranging from >11 to >270, with no clear evidence of a threshold effect within the ranges studied.

Incidence and predictors of atrial fibrillation and its impact on long-term survival in patients with supraventricular arrhythmias.

Background Early readmission after PCI is an important contributor to healthcare expenditures and a target for performance measurement. The extent to which 30‐day readmissions after PCI are preventable is unknown yet essential to minimizing their occurrence. Methods and Results PCI patients readmitted to hospital at which PCI was performed within 30 days of discharge at the Massachusetts General Hospital and Brigham and Womens Hospital were identified, and their medical records were independently reviewed by 2 physicians. Each reviewer used an ordinal scale (0, not; 1, possibly; 2, probably; and 3, definitely preventable) to rate clinical preventability, and a total sum score ≥2 was considered preventable. Characteristics of preventable and unpreventable readmissions were compared, and predictors of clinical preventability were assessed by using multivariate logistic regression. Of 9288 PCIs performed, 9081 (97.8%) patients survived to initial hospital discharge and 1007 (11.1%) were readmitted to the index hospital within 30 days. After excluding repeat readmissions, 893 readmissions were reviewed. Fair agreement between physician reviewers was observed (weighted κ statistic 0.44 [95% CI 0.39 to 0.49]). After aggregation of scores, 380 (42.6%) readmissions were deemed preventable and 513 (57.4%) were deemed not preventable. Common causes of preventable readmissions included staged PCI without new symptoms (14.7%), vascular/bleeding complications of PCI (10.0%), and congestive heart failure (9.7%). Conclusions Nearly half of 30‐day readmissions after PCI may have been prevented by changes in clinical decision‐making. Focusing on these readmissions may reduce readmission rates.

Predicting the Presence of an Acute Coronary Lesion Among Patients Resuscitated From Cardiac Arrest

Background—Early readmission after percutaneous coronary intervention is an important quality metric, but prediction models from registry data have only moderate discrimination. We aimed to improve ability to predict 30-day readmission after percutaneous coronary intervention from a previously validated registry-based model. Methods and Results—We matched readmitted to non-readmitted patients in a 1:2 ratio by risk of readmission, and extracted unstructured and unconventional structured data from the electronic medical record, including need for medical interpretation, albumin level, medical nonadherence, previous number of emergency department visits, atrial fibrillation/flutter, syncope/presyncope, end-stage liver disease, malignancy, and anxiety. We assessed differences in rates of these conditions between cases/controls, and estimated their independent association with 30-day readmission using logistic regression conditional on matched groups. Among 9288 percutaneous coronary interventions, we matched 888 readmitted with 1776 non-readmitted patients. In univariate analysis, cases and controls were significantly different with respect to interpreter (7.9% for cases and 5.3% for controls; P=0.009), emergency department visits (1.12 for cases and 0.77 for controls; P<0.001), homelessness (3.2% for cases and 1.6% for controls; P=0.007), anticoagulation (33.9% for cases and 22.1% for controls; P<0.001), atrial fibrillation/flutter (32.7% for cases and 28.9% for controls; P=0.045), presyncope/syncope (27.8% for cases and 21.3% for controls; P<0.001), and anxiety (69.4% for cases and 62.4% for controls; P<0.001). Anticoagulation, emergency department visits, and anxiety were independently associated with readmission. Conclusions—Patient characteristics derived from review of the electronic health record can be used to refine risk prediction for hospital readmission after percutaneous coronary intervention.

Short-term rehospitalization across the spectrum of age and insurance types in the United States

AIMS To determine the incidence and predictors of atrial fibrillation (AF) and its impact on survival in patients with other forms of supraventricular arrhythmias (SVAs) including atrial flutter (AFL), atrial tachycardia (AT), atrioventricular reentrant (AVRT), and AV nodal reentrant tachycardia (AVNRT). We hypothesized that SVA may increase risk of AF and concomitant AF may influence long-term survival. METHODS AND RESULTS All patients who underwent catheter ablation for SVA from 2000 to 2010 were included in this study. The patients were identified retrospectively and the vital status determined prospectively. Observed survival in the study cohort was compared with survival rates in the age- and sex-matched general population. The study group included 1573 patients (mean age 50.5 ± 18 years, 47% female) with AVNRT (38.5%), AFL (29.6%), AVRT (22.6%) and AT (9.3%). The patients were followed for a mean of 35 months (median 23 months). Atrial fibrillation was documented in 424 patients (27%) with a higher incidence in males (35 vs. 18%). Atrial fibrillation was present in 19.6% of patients before the ablation and developed in 9.07% after ablation. Atrial fibrillation commonly occurred in patients with AFL (57.5%), AT (27.4%), AVRT (13.5%), and AVNRT (9.7%). Older age, prolonged PR interval, dilated left atrium, low left ventricular ejection fraction and presence of AFL were independent predictors for concomitant AF. Long-term survival was worse in the presence of AF. CONCLUSION The incidence of AF is high in patients with other forms of SVA. The most common association is between AFL and AF. Long-term survival is decreased in those who have concomitant AF, although AF did not emerge as an independent predictor of mortality when adjusted for other covariates.

Factors associated with performing urgent coronary angiography in out-of-hospital cardiac arrest patients

Background—A mechanism to stratify patients resuscitated from a cardiac arrest according to the likelihood of an acute coronary lesion would have significant utility. We thus sought to develop and validate a risk prediction model for the presence of an acute coronary lesion among patients resuscitated from an arrest. Methods and Results—All subjects undergoing coronary angiography after resuscitation from a cardiac arrest were identified in an ongoing institutional registry from 2009 to 2014. Backwards stepwise selection of candidate covariates was used to create a logistic regression model for the presence of an angiographic culprit lesion and internally validated with bootstrapping. A clinical point score was generated and its prognostic abilities compared with contemporary measures. Among 247 subjects undergoing coronary angiography after resuscitation from a cardiac arrest, 130 (52%) had an acute lesion in a coronary artery. A multivariable model—including angina, congestive heart failure symptoms, shockable arrest rhythm (ventricular fibrillation/ventricular tachycardia), and ST-elevations—had excellent discrimination (optimism corrected C-Statistic, 0.88) and calibration (Hosmer–Lemeshow P=0.540) for an acute coronary lesion. Compared with electrocardiographic findings alone, a point score based on this model more accurately predicted the presence of an acute lesion among patients resuscitated from a cardiac arrest (integrated discrimination improvement, 0.10; 95% confidence interval, 0.04–0.19; P<0.001). Conclusions—Patients with a cardiac arrest can be risk stratified for the presence of an acute coronary lesion using 4 easily measured variables. This simple risk score may be used to improve patient selection for emergent coronary angiography among resuscitated patients.